Method and system for detection of cardiac arrhythmia

ABSTRACT

There are many different serious cardiac arrhythmias. The present invention uses measurements of RR intervals (interbeat intervals) to detect, in particular but not exclusively, atrial fibrillation of a patient. Atrial fibrilation is a serious ailment in which the heartbeat is generally rapid and irregular. Probability density histograms of ΔRRs (difference between two successive RR intervals) collected during atrial fibrillation of a plurality of subjects are used as a template for the detection of atrial fibrillation. In one implementation, there are 16 standard probability density ΔRRs histograms every 50 ms of mean RR interval of a certain number of beats, where the mean RR interval ranges from 350 ms to 1149 ms. Similarity between the standard probability density histograms and a test density probability histogram of ΔRRs of a patient is estimated by the Kolmogorov-Smirnov test. If the test density histogram is not significantly different from the standard density histogram, atrial fibrillation is detected.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method and a system for detectingcardiac arrhythmias from internally and/or externally detected activityof the heart.

2. Brief Description of the Prior Art

Atrial fibrillation is a serious and common cardiac arrhythmia. Atrialfibrillation is associated with rapid, irregular atrial activation withlife threatening sequelae such as stroke. The atrial activations areirregularly transmitted through the atrioventricular node leading to acorrespondingly irregular sequence of ventricular activations asmonitored by the ventricular interbeat (RR) intervals on the surfaceelectrocardiogram (ECG). An RR interval is an interval between twosuccessive heart beats. Clinically, in the surface ECG, atrialfibrillation is diagnosed by absence of P waves (normally associatedwith the near synchronous activation of the atria) and a rapid irregularventricular rate. P waves are difficult to determine automatically andirregular baseline activity of the ECG is common in atrial fibrillation.

Although a number of different methods have been proposed to test foratrial fibrillation based on knowledge of the RR intervals and/or thesurface ECG, the detection of atrial fibrillation based on this datanevertheless poses substantial problems (Murgatroyd, et al.“Identification of Atrial Fibrillation Episodes in AmbulatoryElectrocardiographic Recordings: Validation of a Method for ObtainingLabeled R-R Interval Files,” Pacing and Clinical Electrophysiology,(1995), pp. 1315-1320). In the following description, the mainstrategies that have been proposed to assess atrial fibrillation basedon knowledge of the RR intervals and/or ECG will be briefly reviewed.

Since RR intervals during atrial fibrillation have a larger standarddeviation and a more rapid decay of the autocorrelation function, thereare proposals that the standard deviation and the autocorrelationfunction can be used to distinguish atrial fibrillation from sinusrhythm (Bootsma, et al. “Analysis of RR Intervals in Patients withAtrial Fibrillation at Rest and During Exercise,” Circulation, (1970),pp. 783-794). Since other abnormal rhythms also have a large standarddeviation of RR intervals and a rapid decay of the autocorrelationfunction, these methods are difficult to apply in concrete situations.

Moody and Mark (G. Moody, et al. “A New Method for Detecting AtrialFibrillation Using R-R Intervals,” Computers in Cardiology, (1983), pp.227-230) classify RR intervals as short, long or regular. They thenconstruct a Markov model for the probabilities for transitions betweenRR intervals in each of the three different length classes. Atrialfibrillation data has typical transition probabilities not shared bynormal rhythms or other arrhythmia. Although the Markov model has highsensitivity for detecting atrial fibrillation, it tends to have arelatively low predictive value of a positive test.

Pinciroli and Castelli have investigated the morphology of histograms ofRR intervals collected during atrial fibrillation and other arrhythmia(F. Pinciroli, et al. “Pre-clinical Experimentation of a QuantitativeSynthesis of the Local Variability in the Original R-R Interval Sequencein the Presence of Arrhythmia,” Automedica, (1986), vol.6, pp. 295-317.Pinciroli and Castelli, 1986). They demonstrated that the histograms ofthe ratio between successive RR intervals show characteristicdifferences between normal rhythm and atrial fibrillation. The histogramof the ratio between successive RR intervals is symmetrical to the meanvalue. No quantitative methods were proposed to quantify the symmetry orto use it to develop a quantitative test.

Since the baseline of the ECG is irregular during atrial fibrillation,Slocum (J. Slocum, et al. “Computer Detection of Atrial Fibrillation onthe Surface Electrocardiogram,” Computers in Cardiolody, (1987), pp.253-254) has proposed that the regularity of the baseline, as determinedby the power spectrum of the residual ECG after subtraction of thebaseline of the QRS complexes can be used to detect atrial fibrillation.This method is necessarily sensitive to small amounts of noise thatmight corrupt the baseline of the ECG.

Implantable ventricular and atrial defibrillators are devices thatdistinguish atrial and ventricular fibrillation from other rhythms.Typically, electrodes in these devices record intracardiac activitydirectly from the atria and ventricles. The methods that are used todetect atrial fibrillation in these devices cannot be easily applied torecordings that give information about the timing of the QRS complexes(U.S. Pat. No. 6,144,878, issued to Schroeppel on Nov. 7, 2000, U.S.Pat. No. 6,035,233 issued to Schroeppel on Mar. 7, 2000, U.S. Pat. No.5,749,900 issued to Schroeppel on May 24, 1998, U.S. Pat. No. 6,064,906issued to Langberg et al. on May 16, 2000, U.S. Pat. No. 5,772,604issued to Langberg et al. on Jun. 30, 1998, U.S. Pat. No. 6,061,592issued to Nigam on May 9, 2000, U.S. Pat. No. 5,951,592 issued to Murphyon Sep. 14, 1999, U.S. Pat. No. 5,941,831 issued to Turcoft on Aug. 24,1999, U.S. Pat. No. 5,591,215 issued to Greenhut et al. on Jan. 7,1997).

Analysis of a histogram of the interbeat intervals can be used todiscriminate between ventricular fibrillation and ventriculartachycardia. By counting the number of beats in predetermined intervalclasses, an algorithm identifies a given sequence as ventricularfibrillation or ventricular tachycardia (U.S. Pat. No. 5,330,508 issuedto Gunderson on Jul. 19, 1994). While this patent suggests that theinvention is of value in detecting and treating atrial fibrillation(column 2, lines 29-31), it does not provide specific embodiment fordetecting and treating atrial fibrillation.

Based on the foregoing review of the prior art, it is apparent thatthere is a need to develop a method and a system for determining whetheror not a given recording is atrial fibrillation based on the timing ofthe QRS complexes as measured from an internal and/or external monitor.Assessment of whether a patient is in atrial fibrillation based on thetiming of the QRS complexes would be extremely useful, for example, forassessing the efficacy of specific drugs on a patient fitted with amonitoring device that measures the timing of the QRS complexes.

SUMMARY OF THE INVENTION

In accordance with the present invention there is provided a method fordetecting cardiac arrhythmia of a patient, comprising detecting RRintervals of the patient wherein each RR interval is an interval betweentwo heart beats, constructing standard histograms of ΔRRs collectedduring cardiac arrhythmia of a plurality of subjects wherein each ΔRR isa difference between two successive RR intervals, constructing testhistograms of ΔRRs of the patient from the detected RR intervals of thispatient, and comparing the standard and test histograms to detectwhether the patient suffers from cardiac arrhythmia.

In accordance with preferred embodiments:

-   -   the standard and test histograms are probability density        histograms,    -   a mean value of a given number of successive RR intervals of the        patient is calculated, and a standard probability density        histogram is chosen in relation to this mean value;    -   the comparison of the standard and test histograms comprises        adjusting a specificity-altering and sensitivity-altering        parameter;    -   the comparison of the standard and test histograms comprises:        -   calculating a standard cumulative probability distribution            from the standard ΔRR probability density histograms;        -   calculating a test cumulative probability distribution from            the test ΔRR probability density histograms;        -   computing a deviation between these standard and test            distributions; and        -   detecting cardiac arrhythmia when the computed deviation is            higher than the specificity-altering and            sensitivity-altering parameter.

The present invention also relates to a system for detecting cardiacarrhythmia of a patient, comprising:

-   -   an RR interval detecting monitor detecting RR intervals of the        patient, wherein each RR interval is an interval between two        heart beat;    -   a standard ΔRR histogram storage unit in which are stored        standard histograms of ΔRRs collected during cardiac arrhythmia        of a plurality of subjects, wherein each ΔRR is a difference        between two successive RR intervals;    -   a test ΔRR histogram calculator supplied with the detected RR of        the patient; and    -   a standard and test ΔRR histograms comparator supplied with the        standard and test histograms, this comparator comprising a        detector of cardiac arrhythmia of the patient responsive to the        comparison of the standard and test histograms.

It is within the scope of the present invention to apply the aboveconcept to detection of not only atrial fibrillation but also to othercardiac arrhythmias including in particular but not exclusively atrialflutter, multifocal atrial tachycardia, ventricular tachycardia,premature ventricular contractions, etc., as well as to detection ofother body phenomenon involving electrical activity. It is also withinthe scope of the present invention to use signals other than the RRintervals, histograms other than ΔRR probability density histograms,tests other than the KS test, and series of ΔRRs other than 100, andthat other methods besides the Komogorov-Smirnov test can be used tocompare test histograms with the standard histograms.

The foregoing and other objects, advantages and features of the presentinvention will become more apparent upon reading of the following nonrestrictive description of a preferred embodiment thereof, given by wayof example only with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In the appended drawings:

FIG. 1 are time series showing the RR intervals from subject 202 fromthe MIT-BIH arrhythmia database. The solid line directly under the timeseries of RR intervals shows the assessment of atrial fibrillation(indicated by AF) or non-atrial fibrillation (indicated by N) asreported in the database. The solid line at the bottom of FIG. 1indicates the assessment of atrial fibrillation, indicated by 1, andnon-atrial fibrillation, indicated by 0, based on an algorithm presentedherein.

FIG. 2 is a flow chart illustrating a preferred embodiment of the methodaccording to the present invention, for detecting atrial fibrillationbased on RR intervals.

FIGS. 3 a-3 p are ΔRR standard probability density histograms duringatrial fibrillation. Mean RR intervals are a) 350-399 ms, b) 400-449 ms,c) 450-499 ms, d) 500-549 ms, e) 550-599 ms, f) 600-649 ms, g) 650-699ms, h) 700-749 ms, i) 750-799 ms, j) 800-849 ms, k) 850-899 ms, l)900-949 ms, m) 950-999 ms, n) 1000-1049 ms, o) 1050-1099 ms, and p)1100-1049 ms.

FIG. 4 is the standard deviation of ΔRR which consists of the standardΔRR probability density histogram as a function of mean RR interval.

FIG. 5 illustrates the Kolmogorov-Smirnov (KS) test. A cumulativeprobability distribution based on patient test data is compared with astandard cumulative probability distribution. D is the greatest distancebetween two cumulative probability distributions.

FIGS. 6 a and 6 b show an example of the standard deviation (FIG. 6 a)and the skewness (FIG. 6 b) of a test ΔRR probability density histogram.The line represents the standard deviation (FIG. 6 a) and the skewness(FIG. 6 b) of the standard ΔRR probability density histogram.

FIG. 7 shows the receiver operating characteristic curve (ROC) when thismethod is tested on the MIT-BIH atrial fibrillation/flutter database.The specificity increases with increase in P, while the sensitivitydecreases with an increase in P_(c).

FIG. 8 is a block diagram of a preferred embodiment of the systemaccording to the present invention for implementing the method of FIG.2, for detecting atrial fibrillation based on RR intervals.

FIG. 9 is a block diagram of a preferred embodiment of a test andstandard ΔRR histogram comparator forming part of the system of FIG. 8.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Although the preferred embodiment of the present invention will bedescribed in relation to atrial fibrillation, the same concept can beapplied to detection of other cardiac arrhythmias including inparticular but not exclusively atrial flutter, multifocal atrialtachycardia, ventricular tachycardia, premature ventricularcontractions, etc. This concept can also be applied to detection ofother body phenomenon involving electrical activity.

Data was obtained from the MIT-BIH atrial fibrillation/flutter database.The data contains 300 atrial fibrillation episodes, sampled at 250 Hzfor 10 hours from Holter tapes of 25 subjects. Arrhythmia detection wascarried out by trained observers and was confirmed by an independentevaluation.

FIG. 1 is a typical time series of RR intervals from a patient withatrial fibrillation. Immediately under the recording is a solid markerline 101. When atrial fibrillation occurs this marker line 101 is set toAF; otherwise it is set to N, which indicates a rhythm that is notatrial fibrillation. The graph of FIG. 1 also shows a lower solid line102 indicating the assessment of atrial fibrilation, indicated by 1, andnon-atrial fibrilation, indicated by 0, based on an algorithm accordingto the present invention. At the onset of atrial fibrillation, therhythm dramatically changes to irregular with large fluctuation. Inparoxysmal atrial fibrillation there is sudden starting and stopping ofatrial fibrillation.

FIG. 2 shows a flow chart of a preferred embodiment of the methodaccording to the invention for detecting atrial fibrillation. FIG. 8 isa block diagram of a preferred embodiment of the system according to theinvention for implementing this method.

The standard ΔRR probability density histograms are prepared asdescribed hereinafter before the detection of atrial fibrillation, andthen stored in an adequate storage unit 804 (FIG. 8).

RR intervals of the patient are first detected (201 of FIG. 2) throughan internal and/or external RR interval monitor 801 (FIG. 8) detectingelectrical activity of the heart beat of the patient.

ΔRR is defined as the difference between two successive RR intervals. Inthe preferred embodiment, blocks of 100 successive RR intervals areprocessed during atrial fibrillation. For that purpose, the detected RRintervals from the monitor 801 are counted (202 of FIG. 2) by a RRinterval counter 802 (FIG. 8) until the number of detected RR intervalsreaches 100 intervals (203 of FIG. 2).

The mean value of each block of 100 RR intervals is computed (204 ofFIG. 2) by means of a calculator 803 from the RR intervals from themonitor 801. Of course, the calculator 803 is supplied with the countfrom the counter 802. This mean value identifies the block of 100 RRintervals as falling into one of sixteen (16) different classes,respectively corresponding to mean values of RR between 350-399 ms,400-449 ms, 450-499 ms, 500-549 ms, 550-599 ms, 600-649 ms, 650-699 ms,700 -749 ms, 750-799 ms, 800-849 ms, 850-899 ms, 900-949 ms, 950-999 ms,1000-1049 ms, 1050-1099 ms, and 1100-1049 ms. For each of the sixteen(16) classes, a standard ΔRR probability density histogram is compiledby lumping data together from all the subjects, for example the subjectsof the above mentioned MIT-BIH atrial fibrilation/flutter database. Theresulting histograms (see for example in FIGS. 3 a-3 p) are taken to bethe standard ΔRR probability density histograms for atrial fibrillation,sorted by the mean RR interval (see for example in FIGS. 3 a-3 p) andstored in storage unit 804. In other words, a standard ΔRR histogramselector 805 chooses the standard ΔRR probability density histogram(FIGS. 3 a-3 p) corresponding to the class in which the computed meanvalue of RR intervals (from 204 in FIG. 2) of the block of 100 RRintervals under consideration falls (205 of FIG. 2).

Obviously, it is within the scope of the present invention to constructthe standard ΔRR probability density histograms using a different numberof consecutive RR intervals, for example 25, 50 or any other number ofconsecutive RR intervals. It is also within the scope of the presentinvention to construct the standard ΔRR probability density histogramsusing mean RR intervals that lie in other ranges, for example 300-399ms, 400-499 ms, 500-599 ms, etc.

FIG. 4 shows the standard deviation (SD) of the standard probabilitydensity histograms of ΔRR.

Test ΔRR probability density histograms are constructed (206 of FIG. 2)by a calculator 806 from the data obtained from the patient (testrecord) through the monitor 801. As indicated in the foregoingdescription, the blocks of 100 successive RR intervals are determined bythe counter 802. In order to test for atrial fibrillation in a testrecord, the test ΔRR probability density histograms based on the blocksof 100 successive RR intervals from the test record, are compared (207and 208) through a comparator 807 to the chosen standard ΔRR probabilitydensity histograms from selector 805.

In the test ΔRR histogram calculator 806 a sequence of 100 RR intervalsis centered on each beat in turn, and the relevant test ΔRR probabilitydensity histograms are calculated. Also, a standard cumulativeprobability distribution is calculated by integrating the area under thecurves of the standard ΔRR probability density histograms, and a testcumulative probability distribution is computed by integrating the areaunder the curves of the test ΔRR probability density histograms (FIG.5).

The similarities between the test histograms for a given patient and thestandard histograms are evaluated in the test and standard ΔRR histogramcomparator 807 using the above mentioned Kolmogorov-Smirnov (KS) test(207 and 208 of FIG. 2). As indicated, FIG. 5 shows an example ofcumulative probability distributions of standard histograms (standardcurve) and test histograms (test curve).

Referring to FIG. 9, a calculator 901 (FIG. 9) computes the cumulativeprobability distribution of the standard probability density ΔRRhistograms. A calculator 902 computes the cumulative probabilitydistribution of test probability density ΔRR histograms. According tothe KS test, one assesses if two given distributions are different fromeach other. In other words, the greatest vertical distance D between thetwo cumulative probability distributions is measured by a calculator 903which returns a p value in the following manner:${p \equiv {Q(\lambda)}} = {2{\sum\limits_{j = 1}^{\infty}{( {- 1} )^{j - 1}{\mathbb{e}}^{{- 2}j^{2}\lambda^{2}}}}}$where λ=({square root}{square root over (N _(e))}+0.12+0.11/{squareroot}{square root over (N _(e))})*D.$N_{e} = {\frac{N_{1}N_{2}}{N_{1} + N_{2}}.}$N₁ is the number of data points on the standard cumulative probabilitydistribution. N₂ is the number of data points in the test cumulativeprobability distribution. A detector 904 determines whether the p valueis greater than a certain, appropriately selected threshold P_(c), anddetection of p>P_(c) indicates that the cumulative probabilitydistributions are not significantly different from one another. Sincethe standard ΔRR probability density histograms is representive ofatrial fibrillation, a value of p>P_(c) constitutes a positiveidentification of atrial fibrillation (or more accurately failure toreject the hypothesis that the test cumulative probability distributionis not atrial fibrillation) (208 in FIG. 2).

FIGS. 6 a and 6 b show a comparison of the ΔRR probability densityhistograms in terms of standard deviation and skewness. A small Ddefined above indicates that the standard deviation and the skewness ofa test ΔRR probability density histogram are clustered around those ofthe standard ΔRR probability density histograms.

The results were assessed by four categories as followed: true positive(TP)—atrial fibrillation is classified as atrial fibrillation; truenegative (TN)—non-atrial fibrillation is classified as non-atrialfibrillation; false negative (FN)—atrial fibrillation is classified asnon-atrial fibrillation; false positive (FP)—non-atrial fibrillation isclassified as atrial fibrillation. Sensitivity and specificity aredefined by TP/(TP+FN) and TN/(TN+FP), respectively. The predictive valueof a positive test (PV+) and the predictive value of a negative test(PV−) are defined by TP/(TP+FP) and TN/(TN+FN), respectively.

The receiver operating characteristic curve (ROC) gives the sensitivityand the specificity in the artrial fibrillation detection algorithm.Variation of the value of P_(c) determines the ROC. FIG. 7 shows the ROCof the assessment of the KS test for the MIT-BIH atrialfibrillation/flutter database. Reducing P_(c), the sensitivity increasesand the specificity decreases. Assuming P_(c)=0.003944, the sensitivityis 96.5%, the specificity is 96.5%, the PV+ is 95.2% and PV− is 97.5%.P_(c) is therefore a sensitivity-altering and specificity-alteringparameter.

It will appear to those of ordinary skill in the art that the method ofFIG. 2 and the system of FIG. 8 can be implemented through a properlyprogrammed computer.

Although the present invention has been described hereinabove by way ofa preferred embodiment thereof, this embodiment can be modified at will,within the scope of the appended claims, without departing from thespirit and nature of the subject invention.

1. A method for detecting cardiac arrhythmia of a patient, comprising:detecting RR intervals of the patient, wherein each RR interval is aninterval between two heart beats; constructing standard histograms ofΔRRs collected during cardiac arrhythmia of a plurality of subjects,wherein each ΔRR is a difference between two successive RR intervals;constructing test histograms of ΔRRs of the patient from the detected RRintervals of said patient; and comparing said standard and testhistograms to detect whether said patient suffers from cardiacarrhythmia.
 2. A method for detecting cardiac arrhythmia as defined inclaim 1, wherein the standard and test histograms are probabilitydensity histograms.
 3. A method for detecting cardiac arrhythmia asdefined in claim 1, further comprising calculating a mean value of agiven number of successive RR intervals of the patient, and choosing oneof the standard probability density histograms in relation to said meanvalue.
 4. A method for detecting cardiac arrhythmia as defined in claim3, wherein choosing one of the standard probability density histogramscomprises selecting the standard histogram corresponding to the range ofRR intervals in which said mean value is located.
 5. A method fordetecting cardiac arrhythmia as defined in claim 1, wherein saidcomparing of the standard and test histograms comprises adjusting aspecificity-altering and sensitivity-altering parameter.
 6. A method fordetecting cardiac arrhythmia as defined in claim 1, wherein saidcomparing of the standard and test histograms comprises calculating astandard cumulative probability distribution from said standard ΔRRprobability density histograms, calculating a test cumulativeprobability distribution from said test ΔRR probability densityhistograms, and computing a deviation between said standard and testdistributions.
 7. A method for detecting cardiac arrhythmia as definedin claim 5, wherein said comparing of the standard and test histogramscomprises: calculating a standard cumulative probability distributionfrom said standard ΔRR probability density histograms; calculating atest cumulative probability distribution from said test ΔRR probabilitydensity histograms; computing a deviation between said standard and testdistributions; and detecting cardiac arrhythmia when the computeddeviation is higher than said specificity-altering andsensitivity-altering parameter.
 8. A system for detecting cardiacarrhythmia of a patient, comprising: a RR interval detecting monitordetecting RR intervals of the patient, wherein each RR interval is aninterval between two heart beats; a standard ΔRR histogram storage unitin which are stored standard histograms of ΔRRs collected during cardiacarrhythmia of a plurality of subjects, wherein each ΔRR is a differencebetween two successive RR intervals; a test ΔRR histogram calculatorsupplied with the detected RR intervals from the monitor andconstructing test histograms of said ΔRRs of said patient; and astandard and test ΔRR histograms comparator supplied with said standardand test histograms, said comparator comprising a detector of cardiacarrhythmia of the patient responsive to the comparison of said standardand test histograms.
 9. A system for detecting cardiac arrhythmia asdefined in claim 8, wherein the standard and test histograms areprobability density histograms.
 10. A system for detecting cardiacarrhythmia as defined in claim 8, further comprising a calculator of amean value of a given number of successive RR intervals of the patient,and a selector of one of the standard probability density histograms inrelation to said mean value.
 11. A system for detecting cardiacarrhythmia as defined in claim 10, wherein the selector of one of thestandard probability density histograms comprises means for selectingthe standard histogram corresponding to the range of RR intervals inwhich said mean value is located.
 12. A system for detecting cardiacarrhythmia as defined in claim 8, wherein said standard and test ΔRRhistograms comparator comprises an adjustable, specificity-altering andsensitivity-altering parameter.
 13. A system for detecting cardiacarrhythmia as defined in claim 8, wherein said standard and test ΔRRhistograms comparator comprises a calculator of a standard cumulativeprobability distribution from said standard ΔRR probability densityhistograms, a calculator of a test cumulative probability distributionfrom said test ΔRR probability density histograms, and a calculator of adeviation between said standard and test distributions.
 14. A method fordetecting cardiac arrhythmia as defined in claim 12, wherein thestandard and test ΔRR histograms comparator comprises: a calculator of astandard cumulative probability distribution from said standard ΔRRprobability density histograms; a calculator of a test cumulativeprobability distribution from said test ΔRR probability densityhistograms; a calculator of a deviation between said standard and testdistributions; and a detector of a cardiac arrhythmia when the computeddeviation is higher than said specificity-altering andsensitivity-altering parameter.